diff --git a/README.md b/README.md
index 5a4ffa64d2..8026e4feb6 100644
--- a/README.md
+++ b/README.md
@@ -37,7 +37,12 @@
 -----
 ## News
 
-[nx-cugraph](./python/nx-cugraph/README.md) is a NetworkX backend that's available to provide GPU acceleration to NetworkX.
+___NEW!___   _[nx-cugraph](./python/nx-cugraph/README.md)_, a NetworkX backend that provides GPU acceleration to NetworkX with zero code change.
+```
+> pip install nx-cugraph-cu11 --extra-index-url https://pypi.nvidia.com
+> export NETWORKX_AUTOMATIC_BACKENDS=cugraph
+```
+That's it.  NetworkX now leverages cuGraph for accelerated graph algorithms.
 
 -----
 
@@ -122,6 +127,7 @@ df_page.sort_values('pagerank', ascending=False).head(10)
 * ArangoDB - a free and open-source native multi-model database system  - https://www.arangodb.com/
 * CuPy - "NumPy/SciPy-compatible Array Library for GPU-accelerated Computing with Python" -  https://cupy.dev/
 * Memgraph - In-memory Graph database - https://memgraph.com/
+* NetworkX (via [nx-cugraph](./python/nx-cugraph/README.md) backend) - an extremely popular, free and open-source package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks - https://networkx.org/
 * PyGraphistry - free and open-source GPU graph ETL, AI, and visualization, including native RAPIDS & cuGraph support - http://github.com/graphistry/pygraphistry
 * ScanPy - a scalable toolkit for analyzing single-cell gene expression data - https://scanpy.readthedocs.io/en/stable/
 
diff --git a/python/nx-cugraph/README.md b/python/nx-cugraph/README.md
index cb0d78954b..148fb2f099 100644
--- a/python/nx-cugraph/README.md
+++ b/python/nx-cugraph/README.md
@@ -6,7 +6,7 @@ to run supported algorithms with GPU acceleration.
 
 ## System Requirements
 
-Using nx-cugraph with this notebook requires the following:
+nx-cugraph requires the following:
 
  * NVIDIA GPU, Pascal architecture or later
  * CUDA 11.2, 11.4, 11.5, 11.8, or 12.0
@@ -34,6 +34,9 @@ Notes:
 
 ## Enabling nx-cugraph
 
+NetworkX will use nx-cugraph as the graph analytics backend if any of the
+following are are used:
+
 ### `NETWORKX_AUTOMATIC_BACKENDS` environment variable.
 The `NETWORKX_AUTOMATIC_BACKENDS` environment variable can be used to have NetworkX automatically dispatch to specified backends an API is called that the backend supports.
 Set `NETWORKX_AUTOMATIC_BACKENDS=cugraph` to use nx-cugraph to GPU accelerate supported APIs with no code changes.
@@ -77,3 +80,56 @@ nxcg_G = nxcg.from_networkx(G)             # conversion happens once here
 nx.betweenness_centrality(nxcg_G, k=1000)  # nxcg Graph type causes cugraph backend
                                            # to be used, no conversion necessary
 ```
+
+## Supported Algorithms
+
+The nx-cugraph backend to NetworkX connects
+[pylibcugraph](../../readme_pages/pylibcugraph.md) (cuGraph's low-level python
+interface to its CUDA-based graph analytics library) and
+[CuPy](https://cupy.dev/) (a GPU-accelerated array library) to NetworkX's
+familiar and easy-to-use API.
+
+Below is the list of algorithms (many listed using pylibcugraph names),
+available today in pylibcugraph or implemented using CuPy, that are or will be
+supported in nx-cugraph.
+
+| feature/algo | release/target version |
+| ----- | ----- |
+| analyze_clustering_edge_cut | ? |
+| analyze_clustering_modularity | ? |
+| analyze_clustering_ratio_cut | ? |
+| balanced_cut_clustering | ? |
+| betweenness_centrality | 23.10 |
+| bfs | ? |
+| core_number | ? |
+| degree_centrality | 23.12 |
+| ecg | ? |
+| edge_betweenness_centrality | 23.10 |
+| ego_graph | ? |
+| eigenvector_centrality | 23.12 |
+| get_two_hop_neighbors | ? |
+| hits | 23.12 |
+| in_degree_centrality | 23.12 |
+| induced_subgraph | ? |
+| jaccard_coefficients | ? |
+| katz_centrality | 23.12 |
+| k_core | ? |
+| k_truss_subgraph | 23.12 |
+| leiden | ? |
+| louvain | 23.10 |
+| node2vec | ? |
+| out_degree_centrality | 23.12 |
+| overlap_coefficients | ? |
+| pagerank | 23.12 |
+| personalized_pagerank | ? |
+| sorensen_coefficients | ? |
+| spectral_modularity_maximization | ? |
+| sssp | 23.12 |
+| strongly_connected_components | ? |
+| triangle_count | ? |
+| uniform_neighbor_sample | ? |
+| uniform_random_walks | ? |
+| weakly_connected_components | ? |
+
+To request nx-cugraph backend support for a NetworkX API that is not listed
+above, visit the [cuGraph GitHub repo](https://github.com/rapidsai/cugraph).